Companies like Amazon take advantage of the fact that they know a whole lot more about buying patterns than you do. As author and entrepreneur Jerry Kaplan explains, this sort of information asymmetry is the real crux of their business plan. Jerry Kaplan's latest book is "Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence" (http://goo.gl/bSVV8K).
Read more at BigThink.com: http://bigthink.com/videos/jerry-kaplan-on-how-amazon-gets-your-money
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - If you’ve ever been online and if you haven’t I don’t know what you’re doing watching this video. You know that many websites are tracking and studying your behavior and in a way they help you by presenting products and information that they think that they believe based upon your browsing history and other characteristics are going to be of great interest to you. But there’s also a darker side to that activity. While that may add great convenience to you the truth is that that also permits them to look at questions like what do they estimate you’re willing to pay for that product? Now a lot of people think mistakenly that you’re supposed to charge the same price for a product to everybody. That’s not the case. You can’t discriminate based on certain criteria – race, religion, sexual preference. But it’s perfectly fine for me to charge this guy more than that guy because I think he’ll pay more and just look at airplane tickets as a perfect example of that sort of thing. Now here’s the problem. We’re taking those kinds of decisions in these websites. Amazon itself is a fantastic example of this and we’re incorporating very sophisticated machine run algorithms that are designed to manage the overall behavior of the group of people who are visiting that website.
In order to optimize profitability for the companies that are running those websites. And they will cut you the least slice of pie, small slice of pie that they can to get you to send you to do what they want you to do in order to maximize the profits of the corporation. Now you may have been on Amazon and you may put things in – I use what’s called a save for later or something in your cart. And you come back the next day and good news, you know, this book is three cents less or that’s two cents more or this is a dollar more. But there aren’t people doing that. This is a machine learning algorithm. And what it’s doing is analyzing time of day and the characteristics of what you bought in the past and how you’ve respond to different kinds of incentives. And where you came from and what kind of browser you’re using as a major factor. Anything it can in order to adjust the price to just the point where you’re going to buy at the highest possible price. You as an individual have freedom of choice. It’s a free country. Buy it. You cannot buy it. That’s great. But we as a group as a set of customers purchasing from Amazon or some other site we adhere to certain statistical properties. So as a group we don’t have that freedom because it can be managed by the entity on the other side. Whenever there’s an information asymmetry like that they know what you’re likely to buy by what your characteristics are and they can optimize the yield on site based upon that. They’re at an advantage over you. Amazon is a wonderful company but it is basically one giant machine learning algorithm. It is designed to do what’s called arbitrage. It knows what it can buy things for. It knows what it can sell things for. And it can adjust the profitability in that zone in order to maximize sales, in order to maximize profits.
And it can do so in a way that is far more efficient than has ever been possible in retailing before. So when I think of Amazon the fact that they’re selling goods is incidental. I think of it like a stock trading programs. Buy low, sell high. Buy here, sell there. There’s a spread. These really are arbitrage systems and you are the mechanism by which these companies maximize their profits.

The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world examples to explore how Excel’s Solver functionality can be used to calculate the optimal price for any product or service.
Downloadable slides are available from SlideShare at http://goo.gl/yGmGfq

published:16 Sep 2014

views:30760

In this new video, Mabel McLean speaks about how two of retail’s largest players are competing against each other. Though both have reputations as low-cost players, Amazon is making unprecedented investments in fulfillment. In the past year, it rolled out free two-day shipping for Prime members and same-day shipping in six US cities. In the summer of 2014, the rollout of Prime Pantry enabled Amazon to ship CPG products profitably in bulk.
Meanwhile, Walmart has adopted an omnichannel strategy. Its ship-to-store program allows consumers to get free shipping in exchange for picking up their items at a store. The brand is also piloting Walmart To-Go, a program that allows users to order their groceries online and pick up at the store in a two to three-hour window.
McLean believes an omnichannel strategy is more effective than one focused on online fulfillment.
View More: http://goo.gl/IIk6LV
Signup for updates: http://goo.gl/kVJTQ3
Follow us on LinkedIn: https://www.linkedin.com/company/l2-inc-
Follow us on Twitter: https://twitter.com/L2_Digital
Follow us on Facebook: https://www.facebook.com/l2inc

published:26 Nov 2014

views:9193

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "MindMath" from Dr. Garg
https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

published:07 Nov 2015

views:98495

Airline fares often seem random, but Richard Quest is on a mission to discover the truth behind airfares.

published:16 Sep 2016

views:614228

Michael Rechenthin, Ph.D. (Dr. Data) explained how all models rely on previously known data such as market prices. Last week in the segment on July 7, 2016, "Predicting (Extrapolating) the Future" Dr. Data explained the science based method of attempting to predict the future which is known as predictive modeling. Today’s segment is part 1 of a planned two part series. Mike uses his visual tools of Data Science to make the intimidating subject of regression algorithms and what information we can glean from them that is most important, easily understood.
A list of information used in models to help them predict future price was displayed in a table. The types of information listed were, historical price data, sector the underlying is in, near a yearly high/low, historical historical Implied Volatility (IV), past month’s price range, news out, near earnings, and outside of the 1 Standard Deviation price range. Dr. Data then noted that most researchers start with regression algorithms. They try to predict prices in the future, while minimizing any errors.
Using a series of graphs Mike contrasted two different models. One was much better at coming closer to the actual price. It’serror, the eventual actual price minus the predicted price was small. Dr. Data explained that the second model, in which the error level was much higher, was actually much better because it more accurately predicted direction. Mike demonstrated why direction is much more important. Predicting the magnitude of the move correctly is great but we can be way off on the magnitude and still make money as long as we get direction right. That’s the big problem with regression algorithms.
Watch this segment of The Skinny On Options Data Science with Tom Sosnoff, Tony Battista and Dr. Data (Michael Rechenthin, Ph.D) as Mike explains the weakness of regression algorithms and the importance of direction over a lower magnitude of error and he does it in a way that is understandable to all tastytraders.
======== tastytrade.com ========
Hosted by Tom Sosnoff and Tony Battista, tastytrade is a real financial network with 8 hours of live programming five days a week during market hours. From pop culture to advanced investment strategies, tastytrade has a broad spectrum of content for viewers of all kinds! Tune in and learn how to trade options successfully and make the most of your investments! Watch tastytrade LIVE daily Monday-Friday 7am-3:30pmCT: http://ow.ly/EbzUU
Subscribe to our YouTube channel: https://www.youtube.com/user/tastytrade1?sub_confirmation=1
Follow tastytrade:
Twitter: https://twitter.com/tastytrade
Facebook: https://www.facebook.com/tastytrade
LinkedIn: http://www.linkedin.com/company/tastytrade
Instagram: http://instagram.com/tastytrade
Pinterest: http://www.pinterest.com/tastytrade/

published:08 Aug 2016

views:4650

This video shows how to solve the following problem.
Min Z = 5x1 + x2
s.t.
2x1 + x2 ≥ 6
X1 + x2 ≥ 4
2x1 + 10x2 ≥ 20
X1, x2 ≥ 0
a) Graphically solve the linear programming problem and determine the optimal solution.
b) What is the objective function value?
c) Calculate slack/surplus for each constraint.
d) If the RHS of Constraint #1 increases by 1, by how much would the OFV change?
e) If the RHS of Constraint #2 increases by 2, by how much would the OFV change?
f) Suppose x1 and x2 are required to be integers, what will the optimal solution be?

published:03 Aug 2016

views:42590

Pricing Decision Analytics | Ryan Air CaseStudy. The above video is from JigsawAcademy - Analytics for Leaders course- http://www.jigsawacademy.com/analytics-for-leaders. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

published:14 Oct 2015

views:4567

VP of Product, Joe Zadeh, talks to updates to the calendar function and reveals Airbnb's new Smart Pricing function and explains how it can help hosts set more informed prices throughout the year to help them maximize their income potential.
SUBSCRIBE: https://www.youtube.com/channel/UCCww-R0oM_CQWXerBcNyKKw?sub_confirmation=1
About Airbnb:
Airbnb is a platform that connects people from around the world to incredible places to stay and interesting things to do. Whether an apartment for a night, a castle for a week, or salsa lessons in Havana to help you truly live there. The Airbnb community aims to create a world where all 7.5 billion people can belong anywhere.
Connect with Airbnb:
Visit Airbnb Website: https://www.airbnb.com
Like Airbnb on FACEBOOK: https://www.facebook.com/airbnb/
Follow Airbnb on TWITTER: https://twitter.com/airbnb
Follow Airbnb on INSTAGRAM: https://www.instagram.com/airbnb/
Follow Airbnb on LINKEDIN: https://www.linkedin.com/company-beta/309694/
Follow Airbnb on PINTEREST: https://www.pinterest.com/airbnb/
Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb
https://www.youtube.com/user/Airbnb

published:08 Dec 2015

views:3670

We discuss how airlines maximize revenue by creating several fares in the same market

An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.

Founded in 1926 by the Radio Corporation of America (RCA), NBC is the oldest major broadcast network in the United States. In 1986, control of NBC passed to General Electric (GE) – which previously owned RCA and NBC until 1930, when it was forced to sell the companies as a result of antitrust charges – through its $6.4 billion purchase of RCA. Following the acquisition by GE (which later liquidated RCA), Bob Wright served as chief executive officer of NBC, remaining in that position until his retirement in 2007, when he was succeeded by Jeff Zucker. In 2003, French media company Vivendi merged its entertainment assets with GE, forming NBCUniversal. Comcast purchased a controlling interest in the company in 2011, and acquired General Electric's remaining stake in 2013. Following the Comcast merger, Zucker left NBCUniversal and was replaced as CEO by Comcast executive Steve Burke.

How Amazon’s Algorithm Gets You to Spend Money

Companies like Amazon take advantage of the fact that they know a whole lot more about buying patterns than you do. As author and entrepreneur Jerry Kaplan explains, this sort of information asymmetry is the real crux of their business plan. Jerry Kaplan's latest book is "Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence" (http://goo.gl/bSVV8K).
Read more at BigThink.com: http://bigthink.com/videos/jerry-kaplan-on-how-amazon-gets-your-money
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - If you’ve ever been online and if you haven’t I don’t know what you’re doing watching this video. You know that many websites are tracking and studying your behavior and in a way they help you by presenting products and information that they think that they believe based upon your browsing history and other characteristics are going to be of great interest to you. But there’s also a darker side to that activity. While that may add great convenience to you the truth is that that also permits them to look at questions like what do they estimate you’re willing to pay for that product? Now a lot of people think mistakenly that you’re supposed to charge the same price for a product to everybody. That’s not the case. You can’t discriminate based on certain criteria – race, religion, sexual preference. But it’s perfectly fine for me to charge this guy more than that guy because I think he’ll pay more and just look at airplane tickets as a perfect example of that sort of thing. Now here’s the problem. We’re taking those kinds of decisions in these websites. Amazon itself is a fantastic example of this and we’re incorporating very sophisticated machine run algorithms that are designed to manage the overall behavior of the group of people who are visiting that website.
In order to optimize profitability for the companies that are running those websites. And they will cut you the least slice of pie, small slice of pie that they can to get you to send you to do what they want you to do in order to maximize the profits of the corporation. Now you may have been on Amazon and you may put things in – I use what’s called a save for later or something in your cart. And you come back the next day and good news, you know, this book is three cents less or that’s two cents more or this is a dollar more. But there aren’t people doing that. This is a machine learning algorithm. And what it’s doing is analyzing time of day and the characteristics of what you bought in the past and how you’ve respond to different kinds of incentives. And where you came from and what kind of browser you’re using as a major factor. Anything it can in order to adjust the price to just the point where you’re going to buy at the highest possible price. You as an individual have freedom of choice. It’s a free country. Buy it. You cannot buy it. That’s great. But we as a group as a set of customers purchasing from Amazon or some other site we adhere to certain statistical properties. So as a group we don’t have that freedom because it can be managed by the entity on the other side. Whenever there’s an information asymmetry like that they know what you’re likely to buy by what your characteristics are and they can optimize the yield on site based upon that. They’re at an advantage over you. Amazon is a wonderful company but it is basically one giant machine learning algorithm. It is designed to do what’s called arbitrage. It knows what it can buy things for. It knows what it can sell things for. And it can adjust the profitability in that zone in order to maximize sales, in order to maximize profits.
And it can do so in a way that is far more efficient than has ever been possible in retailing before. So when I think of Amazon the fact that they’re selling goods is incidental. I think of it like a stock trading programs. Buy low, sell high. Buy here, sell there. There’s a spread. These really are arbitrage systems and you are the mechanism by which these companies maximize their profits.

Pricing Analytics: Optimizing Price

The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world examples to explore how Excel’s Solver functionality can be used to calculate the optimal price for any product or service.
Downloadable slides are available from SlideShare at http://goo.gl/yGmGfq

2:08

Insights on Amazon's Dynamic Pricing

Insights on Amazon's Dynamic Pricing

Insights on Amazon's Dynamic Pricing

In this new video, Mabel McLean speaks about how two of retail’s largest players are competing against each other. Though both have reputations as low-cost players, Amazon is making unprecedented investments in fulfillment. In the past year, it rolled out free two-day shipping for Prime members and same-day shipping in six US cities. In the summer of 2014, the rollout of Prime Pantry enabled Amazon to ship CPG products profitably in bulk.
Meanwhile, Walmart has adopted an omnichannel strategy. Its ship-to-store program allows consumers to get free shipping in exchange for picking up their items at a store. The brand is also piloting Walmart To-Go, a program that allows users to order their groceries online and pick up at the store in a two to three-hour window.
McLean believes an omnichannel strategy is more effective than one focused on online fulfillment.
View More: http://goo.gl/IIk6LV
Signup for updates: http://goo.gl/kVJTQ3
Follow us on LinkedIn: https://www.linkedin.com/company/l2-inc-
Follow us on Twitter: https://twitter.com/L2_Digital
Follow us on Facebook: https://www.facebook.com/l2inc

11:33

Predicting Stock Price Mathematically

Predicting Stock Price Mathematically

Predicting Stock Price Mathematically

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "MindMath" from Dr. Garg
https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

10:03

The science behind airfare pricing

The science behind airfare pricing

The science behind airfare pricing

Airline fares often seem random, but Richard Quest is on a mission to discover the truth behind airfares.

Michael Rechenthin, Ph.D. (Dr. Data) explained how all models rely on previously known data such as market prices. Last week in the segment on July 7, 2016, "Predicting (Extrapolating) the Future" Dr. Data explained the science based method of attempting to predict the future which is known as predictive modeling. Today’s segment is part 1 of a planned two part series. Mike uses his visual tools of Data Science to make the intimidating subject of regression algorithms and what information we can glean from them that is most important, easily understood.
A list of information used in models to help them predict future price was displayed in a table. The types of information listed were, historical price data, sector the underlying is in, near a yearly high/low, historical historical Implied Volatility (IV), past month’s price range, news out, near earnings, and outside of the 1 Standard Deviation price range. Dr. Data then noted that most researchers start with regression algorithms. They try to predict prices in the future, while minimizing any errors.
Using a series of graphs Mike contrasted two different models. One was much better at coming closer to the actual price. It’serror, the eventual actual price minus the predicted price was small. Dr. Data explained that the second model, in which the error level was much higher, was actually much better because it more accurately predicted direction. Mike demonstrated why direction is much more important. Predicting the magnitude of the move correctly is great but we can be way off on the magnitude and still make money as long as we get direction right. That’s the big problem with regression algorithms.
Watch this segment of The Skinny On Options Data Science with Tom Sosnoff, Tony Battista and Dr. Data (Michael Rechenthin, Ph.D) as Mike explains the weakness of regression algorithms and the importance of direction over a lower magnitude of error and he does it in a way that is understandable to all tastytraders.
======== tastytrade.com ========
Hosted by Tom Sosnoff and Tony Battista, tastytrade is a real financial network with 8 hours of live programming five days a week during market hours. From pop culture to advanced investment strategies, tastytrade has a broad spectrum of content for viewers of all kinds! Tune in and learn how to trade options successfully and make the most of your investments! Watch tastytrade LIVE daily Monday-Friday 7am-3:30pmCT: http://ow.ly/EbzUU
Subscribe to our YouTube channel: https://www.youtube.com/user/tastytrade1?sub_confirmation=1
Follow tastytrade:
Twitter: https://twitter.com/tastytrade
Facebook: https://www.facebook.com/tastytrade
LinkedIn: http://www.linkedin.com/company/tastytrade
Instagram: http://instagram.com/tastytrade
Pinterest: http://www.pinterest.com/tastytrade/

5:18

Linear Programming - Shadow Price, Slack/Surplus calculations

Linear Programming - Shadow Price, Slack/Surplus calculations

Linear Programming - Shadow Price, Slack/Surplus calculations

This video shows how to solve the following problem.
Min Z = 5x1 + x2
s.t.
2x1 + x2 ≥ 6
X1 + x2 ≥ 4
2x1 + 10x2 ≥ 20
X1, x2 ≥ 0
a) Graphically solve the linear programming problem and determine the optimal solution.
b) What is the objective function value?
c) Calculate slack/surplus for each constraint.
d) If the RHS of Constraint #1 increases by 1, by how much would the OFV change?
e) If the RHS of Constraint #2 increases by 2, by how much would the OFV change?
f) Suppose x1 and x2 are required to be integers, what will the optimal solution be?

2:09

Ryan Air Case Study: Pricing Decision Analytics

Ryan Air Case Study: Pricing Decision Analytics

Ryan Air Case Study: Pricing Decision Analytics

Pricing Decision Analytics | Ryan Air CaseStudy. The above video is from JigsawAcademy - Analytics for Leaders course- http://www.jigsawacademy.com/analytics-for-leaders. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

8:37

Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb

Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb

Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb

VP of Product, Joe Zadeh, talks to updates to the calendar function and reveals Airbnb's new Smart Pricing function and explains how it can help hosts set more informed prices throughout the year to help them maximize their income potential.
SUBSCRIBE: https://www.youtube.com/channel/UCCww-R0oM_CQWXerBcNyKKw?sub_confirmation=1
About Airbnb:
Airbnb is a platform that connects people from around the world to incredible places to stay and interesting things to do. Whether an apartment for a night, a castle for a week, or salsa lessons in Havana to help you truly live there. The Airbnb community aims to create a world where all 7.5 billion people can belong anywhere.
Connect with Airbnb:
Visit Airbnb Website: https://www.airbnb.com
Like Airbnb on FACEBOOK: https://www.facebook.com/airbnb/
Follow Airbnb on TWITTER: https://twitter.com/airbnb
Follow Airbnb on INSTAGRAM: https://www.instagram.com/airbnb/
Follow Airbnb on LINKEDIN: https://www.linkedin.com/company-beta/309694/
Follow Airbnb on PINTEREST: https://www.pinterest.com/airbnb/
Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb
https://www.youtube.com/user/Airbnb

12:37

Airline Pricing - Part 1 - Fare Structures

Airline Pricing - Part 1 - Fare Structures

Airline Pricing - Part 1 - Fare Structures

We discuss how airlines maximize revenue by creating several fares in the same market

14:10

FROM BIG DATA TO THE ALGORITHMIC ECONOMY, VICTOR ROSENMAN

FROM BIG DATA TO THE ALGORITHMIC ECONOMY, VICTOR ROSENMAN

FROM BIG DATA TO THE ALGORITHMIC ECONOMY, VICTOR ROSENMAN

Victor Rosenman is CEO & Founder of Feedvisor. Before founding Feedvisor, Victor worked as a consultant to high-volume Amazon sellers, helping optimize and automate many aspects of their business. This deep technical understanding of the Amazon Marketplace, coupled with a strong knowledge of computer algorithms, led Victor to establish Feedvisor in 2011.
Feedvisor is the pioneer of Algo-Commerce – the discipline of using Big Data and Machine Learning Algorithms to make business-critical decisions for online retailers.
Feedvisor’s cloud-based Algorithmic Repricing and RevenueIntelligence solutions power millions of pricing decisions daily; providing retailers with actionable insights to maximize profitability and drive their business growth.
www.feedvisor.com

The pricing of airline tickets might seem like a mystery but it’s actually an algorithm. Airline companies use a dynamic pricing algorithm that takes many factors into account, things like supply and demand and historical trends. That’s why your Thanksgiving flights will always cost and an arm and a leg.
» Subscribe to NBCNews: http://nbcnews.to/SubscribeToNBC
» Watch more NBC video: http://bit.ly/MoreNBCNews
NBC News is a leading source of global news and information. Here you will find clips from NBC Nightly News, Meet The Press, and our original series Debunker, Flashback, Nerdwatch, and Show Me. Subscribe to our channel for news stories, technology, politics, health, entertainment, science, business, and exclusive NBC investigations.
Connect with NBC News Online!
Visit NBCNews.Com: ...

published: 01 Dec 2016

How Amazon’s Algorithm Gets You to Spend Money

Companies like Amazon take advantage of the fact that they know a whole lot more about buying patterns than you do. As author and entrepreneur Jerry Kaplan explains, this sort of information asymmetry is the real crux of their business plan. Jerry Kaplan's latest book is "Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence" (http://goo.gl/bSVV8K).
Read more at BigThink.com: http://bigthink.com/videos/jerry-kaplan-on-how-amazon-gets-your-money
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - If you’ve ever been online and if you haven’t I don’t know what you’re doing watching this video. You know that many websites are tracking and studying your be...

The pricing of airline tickets might seem like a mystery but it’s actually an algorithm. Airline companies use a dynamic pricing algorithm that takes many factors into account, things like supply and demand and historical trends. That’s why your holiday flights will always cost and an arm and a leg.
» Subscribe to NBCNews: http://nbcnews.to/SubscribeToNBC
» Watch more NBC video: http://bit.ly/MoreNBCNews
NBC News is a leading source of global news and information. Here you will find clips from NBC Nightly News, Meet The Press, and original digital videos. Subscribe to our channel for news stories, technology, politics, health, entertainment, science, business, and exclusive NBC investigations.
Connect with NBC News Online!
Visit NBCNews.Com: http://nbcnews.to/ReadNBC
Find NBC News on Fa...

published: 20 Nov 2017

Pricing Analytics: Optimizing Price

The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world examples to explore how Excel’s Solver functionality can be used to calculate the optimal price for any product or service.
Downloadable slides are available from SlideShare at http://goo.gl/yGmGfq

published: 16 Sep 2014

Insights on Amazon's Dynamic Pricing

In this new video, Mabel McLean speaks about how two of retail’s largest players are competing against each other. Though both have reputations as low-cost players, Amazon is making unprecedented investments in fulfillment. In the past year, it rolled out free two-day shipping for Prime members and same-day shipping in six US cities. In the summer of 2014, the rollout of Prime Pantry enabled Amazon to ship CPG products profitably in bulk.
Meanwhile, Walmart has adopted an omnichannel strategy. Its ship-to-store program allows consumers to get free shipping in exchange for picking up their items at a store. The brand is also piloting Walmart To-Go, a program that allows users to order their groceries online and pick up at the store in a two to three-hour window.
McLean believes an omnicha...

published: 26 Nov 2014

Predicting Stock Price Mathematically

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "MindMath" from Dr. Garg
https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

published: 07 Nov 2015

The science behind airfare pricing

Airline fares often seem random, but Richard Quest is on a mission to discover the truth behind airfares.

Michael Rechenthin, Ph.D. (Dr. Data) explained how all models rely on previously known data such as market prices. Last week in the segment on July 7, 2016, "Predicting (Extrapolating) the Future" Dr. Data explained the science based method of attempting to predict the future which is known as predictive modeling. Today’s segment is part 1 of a planned two part series. Mike uses his visual tools of Data Science to make the intimidating subject of regression algorithms and what information we can glean from them that is most important, easily understood.
A list of information used in models to help them predict future price was displayed in a table. The types of information listed were, historical price data, sector the underlying is in, near a yearly high/low, historical historical Implie...

published: 08 Aug 2016

Linear Programming - Shadow Price, Slack/Surplus calculations

This video shows how to solve the following problem.
Min Z = 5x1 + x2
s.t.
2x1 + x2 ≥ 6
X1 + x2 ≥ 4
2x1 + 10x2 ≥ 20
X1, x2 ≥ 0
a) Graphically solve the linear programming problem and determine the optimal solution.
b) What is the objective function value?
c) Calculate slack/surplus for each constraint.
d) If the RHS of Constraint #1 increases by 1, by how much would the OFV change?
e) If the RHS of Constraint #2 increases by 2, by how much would the OFV change?
f) Suppose x1 and x2 are required to be integers, what will the optimal solution be?

published: 03 Aug 2016

Ryan Air Case Study: Pricing Decision Analytics

Pricing Decision Analytics | Ryan Air CaseStudy. The above video is from JigsawAcademy - Analytics for Leaders course- http://www.jigsawacademy.com/analytics-for-leaders. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals
Follow us on:
https://www.facebook.com/jigsawacademy
https://twitter.com/jigsawacademy
http://jigsawacademy.com/

published: 14 Oct 2015

Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb

VP of Product, Joe Zadeh, talks to updates to the calendar function and reveals Airbnb's new Smart Pricing function and explains how it can help hosts set more informed prices throughout the year to help them maximize their income potential.
SUBSCRIBE: https://www.youtube.com/channel/UCCww-R0oM_CQWXerBcNyKKw?sub_confirmation=1
About Airbnb:
Airbnb is a platform that connects people from around the world to incredible places to stay and interesting things to do. Whether an apartment for a night, a castle for a week, or salsa lessons in Havana to help you truly live there. The Airbnb community aims to create a world where all 7.5 billion people can belong anywhere.
Connect with Airbnb:
Visit Airbnb Website: https://www.airbnb.com
Like Airbnb on FACEBOOK: https://www.facebook.com/airbnb/
F...

published: 08 Dec 2015

Airline Pricing - Part 1 - Fare Structures

We discuss how airlines maximize revenue by creating several fares in the same market

published: 04 Sep 2013

FROM BIG DATA TO THE ALGORITHMIC ECONOMY, VICTOR ROSENMAN

Victor Rosenman is CEO & Founder of Feedvisor. Before founding Feedvisor, Victor worked as a consultant to high-volume Amazon sellers, helping optimize and automate many aspects of their business. This deep technical understanding of the Amazon Marketplace, coupled with a strong knowledge of computer algorithms, led Victor to establish Feedvisor in 2011.
Feedvisor is the pioneer of Algo-Commerce – the discipline of using Big Data and Machine Learning Algorithms to make business-critical decisions for online retailers.
Feedvisor’s cloud-based Algorithmic Repricing and RevenueIntelligence solutions power millions of pricing decisions daily; providing retailers with actionable insights to maximize profitability and drive their business growth.
www.feedvisor.com

How Amazon’s Algorithm Gets You to Spend Money

Companies like Amazon take advantage of the fact that they know a whole lot more about buying patterns than you do. As author and entrepreneur Jerry Kaplan expl...

Companies like Amazon take advantage of the fact that they know a whole lot more about buying patterns than you do. As author and entrepreneur Jerry Kaplan explains, this sort of information asymmetry is the real crux of their business plan. Jerry Kaplan's latest book is "Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence" (http://goo.gl/bSVV8K).
Read more at BigThink.com: http://bigthink.com/videos/jerry-kaplan-on-how-amazon-gets-your-money
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - If you’ve ever been online and if you haven’t I don’t know what you’re doing watching this video. You know that many websites are tracking and studying your behavior and in a way they help you by presenting products and information that they think that they believe based upon your browsing history and other characteristics are going to be of great interest to you. But there’s also a darker side to that activity. While that may add great convenience to you the truth is that that also permits them to look at questions like what do they estimate you’re willing to pay for that product? Now a lot of people think mistakenly that you’re supposed to charge the same price for a product to everybody. That’s not the case. You can’t discriminate based on certain criteria – race, religion, sexual preference. But it’s perfectly fine for me to charge this guy more than that guy because I think he’ll pay more and just look at airplane tickets as a perfect example of that sort of thing. Now here’s the problem. We’re taking those kinds of decisions in these websites. Amazon itself is a fantastic example of this and we’re incorporating very sophisticated machine run algorithms that are designed to manage the overall behavior of the group of people who are visiting that website.
In order to optimize profitability for the companies that are running those websites. And they will cut you the least slice of pie, small slice of pie that they can to get you to send you to do what they want you to do in order to maximize the profits of the corporation. Now you may have been on Amazon and you may put things in – I use what’s called a save for later or something in your cart. And you come back the next day and good news, you know, this book is three cents less or that’s two cents more or this is a dollar more. But there aren’t people doing that. This is a machine learning algorithm. And what it’s doing is analyzing time of day and the characteristics of what you bought in the past and how you’ve respond to different kinds of incentives. And where you came from and what kind of browser you’re using as a major factor. Anything it can in order to adjust the price to just the point where you’re going to buy at the highest possible price. You as an individual have freedom of choice. It’s a free country. Buy it. You cannot buy it. That’s great. But we as a group as a set of customers purchasing from Amazon or some other site we adhere to certain statistical properties. So as a group we don’t have that freedom because it can be managed by the entity on the other side. Whenever there’s an information asymmetry like that they know what you’re likely to buy by what your characteristics are and they can optimize the yield on site based upon that. They’re at an advantage over you. Amazon is a wonderful company but it is basically one giant machine learning algorithm. It is designed to do what’s called arbitrage. It knows what it can buy things for. It knows what it can sell things for. And it can adjust the profitability in that zone in order to maximize sales, in order to maximize profits.
And it can do so in a way that is far more efficient than has ever been possible in retailing before. So when I think of Amazon the fact that they’re selling goods is incidental. I think of it like a stock trading programs. Buy low, sell high. Buy here, sell there. There’s a spread. These really are arbitrage systems and you are the mechanism by which these companies maximize their profits.

Companies like Amazon take advantage of the fact that they know a whole lot more about buying patterns than you do. As author and entrepreneur Jerry Kaplan explains, this sort of information asymmetry is the real crux of their business plan. Jerry Kaplan's latest book is "Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence" (http://goo.gl/bSVV8K).
Read more at BigThink.com: http://bigthink.com/videos/jerry-kaplan-on-how-amazon-gets-your-money
FollowBigThink here:
YouTube: http://goo.gl/CPTsV5
Facebook: https://www.facebook.com/BigThinkdotcom
Twitter: https://twitter.com/bigthink
Transcript - If you’ve ever been online and if you haven’t I don’t know what you’re doing watching this video. You know that many websites are tracking and studying your behavior and in a way they help you by presenting products and information that they think that they believe based upon your browsing history and other characteristics are going to be of great interest to you. But there’s also a darker side to that activity. While that may add great convenience to you the truth is that that also permits them to look at questions like what do they estimate you’re willing to pay for that product? Now a lot of people think mistakenly that you’re supposed to charge the same price for a product to everybody. That’s not the case. You can’t discriminate based on certain criteria – race, religion, sexual preference. But it’s perfectly fine for me to charge this guy more than that guy because I think he’ll pay more and just look at airplane tickets as a perfect example of that sort of thing. Now here’s the problem. We’re taking those kinds of decisions in these websites. Amazon itself is a fantastic example of this and we’re incorporating very sophisticated machine run algorithms that are designed to manage the overall behavior of the group of people who are visiting that website.
In order to optimize profitability for the companies that are running those websites. And they will cut you the least slice of pie, small slice of pie that they can to get you to send you to do what they want you to do in order to maximize the profits of the corporation. Now you may have been on Amazon and you may put things in – I use what’s called a save for later or something in your cart. And you come back the next day and good news, you know, this book is three cents less or that’s two cents more or this is a dollar more. But there aren’t people doing that. This is a machine learning algorithm. And what it’s doing is analyzing time of day and the characteristics of what you bought in the past and how you’ve respond to different kinds of incentives. And where you came from and what kind of browser you’re using as a major factor. Anything it can in order to adjust the price to just the point where you’re going to buy at the highest possible price. You as an individual have freedom of choice. It’s a free country. Buy it. You cannot buy it. That’s great. But we as a group as a set of customers purchasing from Amazon or some other site we adhere to certain statistical properties. So as a group we don’t have that freedom because it can be managed by the entity on the other side. Whenever there’s an information asymmetry like that they know what you’re likely to buy by what your characteristics are and they can optimize the yield on site based upon that. They’re at an advantage over you. Amazon is a wonderful company but it is basically one giant machine learning algorithm. It is designed to do what’s called arbitrage. It knows what it can buy things for. It knows what it can sell things for. And it can adjust the profitability in that zone in order to maximize sales, in order to maximize profits.
And it can do so in a way that is far more efficient than has ever been possible in retailing before. So when I think of Amazon the fact that they’re selling goods is incidental. I think of it like a stock trading programs. Buy low, sell high. Buy here, sell there. There’s a spread. These really are arbitrage systems and you are the mechanism by which these companies maximize their profits.

Pricing Analytics: Optimizing Price

The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world ...

The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world examples to explore how Excel’s Solver functionality can be used to calculate the optimal price for any product or service.
Downloadable slides are available from SlideShare at http://goo.gl/yGmGfq

The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world examples to explore how Excel’s Solver functionality can be used to calculate the optimal price for any product or service.
Downloadable slides are available from SlideShare at http://goo.gl/yGmGfq

Insights on Amazon's Dynamic Pricing

In this new video, Mabel McLean speaks about how two of retail’s largest players are competing against each other. Though both have reputations as low-cost play...

In this new video, Mabel McLean speaks about how two of retail’s largest players are competing against each other. Though both have reputations as low-cost players, Amazon is making unprecedented investments in fulfillment. In the past year, it rolled out free two-day shipping for Prime members and same-day shipping in six US cities. In the summer of 2014, the rollout of Prime Pantry enabled Amazon to ship CPG products profitably in bulk.
Meanwhile, Walmart has adopted an omnichannel strategy. Its ship-to-store program allows consumers to get free shipping in exchange for picking up their items at a store. The brand is also piloting Walmart To-Go, a program that allows users to order their groceries online and pick up at the store in a two to three-hour window.
McLean believes an omnichannel strategy is more effective than one focused on online fulfillment.
View More: http://goo.gl/IIk6LV
Signup for updates: http://goo.gl/kVJTQ3
Follow us on LinkedIn: https://www.linkedin.com/company/l2-inc-
Follow us on Twitter: https://twitter.com/L2_Digital
Follow us on Facebook: https://www.facebook.com/l2inc

In this new video, Mabel McLean speaks about how two of retail’s largest players are competing against each other. Though both have reputations as low-cost players, Amazon is making unprecedented investments in fulfillment. In the past year, it rolled out free two-day shipping for Prime members and same-day shipping in six US cities. In the summer of 2014, the rollout of Prime Pantry enabled Amazon to ship CPG products profitably in bulk.
Meanwhile, Walmart has adopted an omnichannel strategy. Its ship-to-store program allows consumers to get free shipping in exchange for picking up their items at a store. The brand is also piloting Walmart To-Go, a program that allows users to order their groceries online and pick up at the store in a two to three-hour window.
McLean believes an omnichannel strategy is more effective than one focused on online fulfillment.
View More: http://goo.gl/IIk6LV
Signup for updates: http://goo.gl/kVJTQ3
Follow us on LinkedIn: https://www.linkedin.com/company/l2-inc-
Follow us on Twitter: https://twitter.com/L2_Digital
Follow us on Facebook: https://www.facebook.com/l2inc

Predicting Stock Price Mathematically

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling pri...

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "MindMath" from Dr. Garg
https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "MindMath" from Dr. Garg
https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

Michael Rechenthin, Ph.D. (Dr. Data) explained how all models rely on previously known data such as market prices. Last week in the segment on July 7, 2016, "Pr...

Michael Rechenthin, Ph.D. (Dr. Data) explained how all models rely on previously known data such as market prices. Last week in the segment on July 7, 2016, "Predicting (Extrapolating) the Future" Dr. Data explained the science based method of attempting to predict the future which is known as predictive modeling. Today’s segment is part 1 of a planned two part series. Mike uses his visual tools of Data Science to make the intimidating subject of regression algorithms and what information we can glean from them that is most important, easily understood.
A list of information used in models to help them predict future price was displayed in a table. The types of information listed were, historical price data, sector the underlying is in, near a yearly high/low, historical historical Implied Volatility (IV), past month’s price range, news out, near earnings, and outside of the 1 Standard Deviation price range. Dr. Data then noted that most researchers start with regression algorithms. They try to predict prices in the future, while minimizing any errors.
Using a series of graphs Mike contrasted two different models. One was much better at coming closer to the actual price. It’serror, the eventual actual price minus the predicted price was small. Dr. Data explained that the second model, in which the error level was much higher, was actually much better because it more accurately predicted direction. Mike demonstrated why direction is much more important. Predicting the magnitude of the move correctly is great but we can be way off on the magnitude and still make money as long as we get direction right. That’s the big problem with regression algorithms.
Watch this segment of The Skinny On Options Data Science with Tom Sosnoff, Tony Battista and Dr. Data (Michael Rechenthin, Ph.D) as Mike explains the weakness of regression algorithms and the importance of direction over a lower magnitude of error and he does it in a way that is understandable to all tastytraders.
======== tastytrade.com ========
Hosted by Tom Sosnoff and Tony Battista, tastytrade is a real financial network with 8 hours of live programming five days a week during market hours. From pop culture to advanced investment strategies, tastytrade has a broad spectrum of content for viewers of all kinds! Tune in and learn how to trade options successfully and make the most of your investments! Watch tastytrade LIVE daily Monday-Friday 7am-3:30pmCT: http://ow.ly/EbzUU
Subscribe to our YouTube channel: https://www.youtube.com/user/tastytrade1?sub_confirmation=1
Follow tastytrade:
Twitter: https://twitter.com/tastytrade
Facebook: https://www.facebook.com/tastytrade
LinkedIn: http://www.linkedin.com/company/tastytrade
Instagram: http://instagram.com/tastytrade
Pinterest: http://www.pinterest.com/tastytrade/

Michael Rechenthin, Ph.D. (Dr. Data) explained how all models rely on previously known data such as market prices. Last week in the segment on July 7, 2016, "Predicting (Extrapolating) the Future" Dr. Data explained the science based method of attempting to predict the future which is known as predictive modeling. Today’s segment is part 1 of a planned two part series. Mike uses his visual tools of Data Science to make the intimidating subject of regression algorithms and what information we can glean from them that is most important, easily understood.
A list of information used in models to help them predict future price was displayed in a table. The types of information listed were, historical price data, sector the underlying is in, near a yearly high/low, historical historical Implied Volatility (IV), past month’s price range, news out, near earnings, and outside of the 1 Standard Deviation price range. Dr. Data then noted that most researchers start with regression algorithms. They try to predict prices in the future, while minimizing any errors.
Using a series of graphs Mike contrasted two different models. One was much better at coming closer to the actual price. It’serror, the eventual actual price minus the predicted price was small. Dr. Data explained that the second model, in which the error level was much higher, was actually much better because it more accurately predicted direction. Mike demonstrated why direction is much more important. Predicting the magnitude of the move correctly is great but we can be way off on the magnitude and still make money as long as we get direction right. That’s the big problem with regression algorithms.
Watch this segment of The Skinny On Options Data Science with Tom Sosnoff, Tony Battista and Dr. Data (Michael Rechenthin, Ph.D) as Mike explains the weakness of regression algorithms and the importance of direction over a lower magnitude of error and he does it in a way that is understandable to all tastytraders.
======== tastytrade.com ========
Hosted by Tom Sosnoff and Tony Battista, tastytrade is a real financial network with 8 hours of live programming five days a week during market hours. From pop culture to advanced investment strategies, tastytrade has a broad spectrum of content for viewers of all kinds! Tune in and learn how to trade options successfully and make the most of your investments! Watch tastytrade LIVE daily Monday-Friday 7am-3:30pmCT: http://ow.ly/EbzUU
Subscribe to our YouTube channel: https://www.youtube.com/user/tastytrade1?sub_confirmation=1
Follow tastytrade:
Twitter: https://twitter.com/tastytrade
Facebook: https://www.facebook.com/tastytrade
LinkedIn: http://www.linkedin.com/company/tastytrade
Instagram: http://instagram.com/tastytrade
Pinterest: http://www.pinterest.com/tastytrade/

This video shows how to solve the following problem.
Min Z = 5x1 + x2
s.t.
2x1 + x2 ≥ 6
X1 + x2 ≥ 4
2x1 + 10x2 ≥ 20
X1, x2 ≥ 0
a) Graphically solve the linear programming problem and determine the optimal solution.
b) What is the objective function value?
c) Calculate slack/surplus for each constraint.
d) If the RHS of Constraint #1 increases by 1, by how much would the OFV change?
e) If the RHS of Constraint #2 increases by 2, by how much would the OFV change?
f) Suppose x1 and x2 are required to be integers, what will the optimal solution be?

This video shows how to solve the following problem.
Min Z = 5x1 + x2
s.t.
2x1 + x2 ≥ 6
X1 + x2 ≥ 4
2x1 + 10x2 ≥ 20
X1, x2 ≥ 0
a) Graphically solve the linear programming problem and determine the optimal solution.
b) What is the objective function value?
c) Calculate slack/surplus for each constraint.
d) If the RHS of Constraint #1 increases by 1, by how much would the OFV change?
e) If the RHS of Constraint #2 increases by 2, by how much would the OFV change?
f) Suppose x1 and x2 are required to be integers, what will the optimal solution be?

VP of Product, Joe Zadeh, talks to updates to the calendar function and reveals Airbnb's new Smart Pricing function and explains how it can help hosts set more informed prices throughout the year to help them maximize their income potential.
SUBSCRIBE: https://www.youtube.com/channel/UCCww-R0oM_CQWXerBcNyKKw?sub_confirmation=1
About Airbnb:
Airbnb is a platform that connects people from around the world to incredible places to stay and interesting things to do. Whether an apartment for a night, a castle for a week, or salsa lessons in Havana to help you truly live there. The Airbnb community aims to create a world where all 7.5 billion people can belong anywhere.
Connect with Airbnb:
Visit Airbnb Website: https://www.airbnb.com
Like Airbnb on FACEBOOK: https://www.facebook.com/airbnb/
Follow Airbnb on TWITTER: https://twitter.com/airbnb
Follow Airbnb on INSTAGRAM: https://www.instagram.com/airbnb/
Follow Airbnb on LINKEDIN: https://www.linkedin.com/company-beta/309694/
Follow Airbnb on PINTEREST: https://www.pinterest.com/airbnb/
Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb
https://www.youtube.com/user/Airbnb

VP of Product, Joe Zadeh, talks to updates to the calendar function and reveals Airbnb's new Smart Pricing function and explains how it can help hosts set more informed prices throughout the year to help them maximize their income potential.
SUBSCRIBE: https://www.youtube.com/channel/UCCww-R0oM_CQWXerBcNyKKw?sub_confirmation=1
About Airbnb:
Airbnb is a platform that connects people from around the world to incredible places to stay and interesting things to do. Whether an apartment for a night, a castle for a week, or salsa lessons in Havana to help you truly live there. The Airbnb community aims to create a world where all 7.5 billion people can belong anywhere.
Connect with Airbnb:
Visit Airbnb Website: https://www.airbnb.com
Like Airbnb on FACEBOOK: https://www.facebook.com/airbnb/
Follow Airbnb on TWITTER: https://twitter.com/airbnb
Follow Airbnb on INSTAGRAM: https://www.instagram.com/airbnb/
Follow Airbnb on LINKEDIN: https://www.linkedin.com/company-beta/309694/
Follow Airbnb on PINTEREST: https://www.pinterest.com/airbnb/
Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb
https://www.youtube.com/user/Airbnb

FROM BIG DATA TO THE ALGORITHMIC ECONOMY, VICTOR ROSENMAN

Victor Rosenman is CEO & Founder of Feedvisor. Before founding Feedvisor, Victor worked as a consultant to high-volume Amazon sellers, helping optimize and auto...

Victor Rosenman is CEO & Founder of Feedvisor. Before founding Feedvisor, Victor worked as a consultant to high-volume Amazon sellers, helping optimize and automate many aspects of their business. This deep technical understanding of the Amazon Marketplace, coupled with a strong knowledge of computer algorithms, led Victor to establish Feedvisor in 2011.
Feedvisor is the pioneer of Algo-Commerce – the discipline of using Big Data and Machine Learning Algorithms to make business-critical decisions for online retailers.
Feedvisor’s cloud-based Algorithmic Repricing and RevenueIntelligence solutions power millions of pricing decisions daily; providing retailers with actionable insights to maximize profitability and drive their business growth.
www.feedvisor.com

Victor Rosenman is CEO & Founder of Feedvisor. Before founding Feedvisor, Victor worked as a consultant to high-volume Amazon sellers, helping optimize and automate many aspects of their business. This deep technical understanding of the Amazon Marketplace, coupled with a strong knowledge of computer algorithms, led Victor to establish Feedvisor in 2011.
Feedvisor is the pioneer of Algo-Commerce – the discipline of using Big Data and Machine Learning Algorithms to make business-critical decisions for online retailers.
Feedvisor’s cloud-based Algorithmic Repricing and RevenueIntelligence solutions power millions of pricing decisions daily; providing retailers with actionable insights to maximize profitability and drive their business growth.
www.feedvisor.com

Dynamic Pricing in Ride-Sharing Platforms

Airbnb Supply and Demand - Dynamic Pricing [Live Q&A]

This is a MUST WATCH webinar!
In the wrap up of our pricing series, you will learn how to:
- Understand the basics of Airbnb supply and demand
- Identify the 3 BIGGEST factors that drive demand in YOUR area
Here is the link to the special offer. www.beyondpricing.com/#r=LEARNAIRBNB
This week's webinar was presented by Ian McHenry from Beyond Pricing.
Also, check out this blog post on How much your Airbnb unit is worth: http://learnairbnb.com/my-airbnb-unit-worth/

published: 10 Jun 2015

Dynamic Pricing - Airplane (Matlab Simulation)

2015 IAAA Finalist Analytics for an Online Retailer

Analytics for an Online Retailer: Demand forecasting and Price Optimization at Rue La La
We partnered with the online flash sales retailer Rue La La to develop and implement a pricing decision support tool that sets initial prices for new products. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts (typically 2-3 days) on designer apparel and accessories. Our approach is two-fold and begins with developing a demand prediction model for new products. The two biggest challenges faced when building our demand prediction model include estimating lost sales due to stockouts, and predicting demand for styles that have no historical sales data. We use descriptive analytics (clustering) and predictive analytics (regression) to address these...

https://www.facebook.com/tusharroy25
https://github.com/mission-peace/interview/blob/master/src/com/interview/dynamic/StockBuySellKTransactions.java
https://github.com/mission-peace/interview/blob/master/python/dynamic/stockbuysellktransactions.py
https://github.com/mission-peace/interview/wiki
Given an array for which the ith element is the price of a given stock on day i.
Design an algorithm to find the maximum profit. You may complete at most k transactions. Transactions cannot occur in parallel. One transaction should complete before starting another transaction.

published: 06 Jan 2016

Lecture 6: Pricing Options with Monte Carlo

Lecturer: Prof. Shimon Benninga
We show how to price Asian and barrier options using MC. A starting point is an extended example of how to use MC to price plain vanilla calls. This example illustrates the basic principles of MC pricing for options.

published: 17 Aug 2011

What Am I Paying For? An Insider Explains Airline Pricing

Derivatives Analytics with Python - O'Reilly Webcast

Originally aired June 24, 2014. In this webcast you will learn how Python can be used for Derivatives Analytics and Financial Engineering. Dr. Yves J. Hilpisch will begin by covering the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. The approach is a practical one in that all-important aspects are illustrated by a set of self-contained Python scripts.
This webcast talk will cover:
Financial Algorithm Implementation
Monte CarloValuation
Binomial Option Pricing
PerformanceLibrariesDynamic Compiling
ParallelCodeExecution
DX Analytics
Overview and Philosophy
Multi-Risk Derivatives Pricing
Global Valuation
Web Technologies ...

Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy

Shoppers with Internet access and a bargain-hunting impulse can find a universe of products at their fingertips. In this thought-provoking exposé, Maurice Stucke and Ariel Ezrachi invite us to take a harder look at today’s app-assisted paradise of digital shopping. While consumers reap many benefits from online purchasing, the sophisticated algorithms and data-crunching that make browsing so convenient are also changing the nature of market competition, and not always for the better.
Computers colluding is one danger. Although long-standing laws prevent companies from fixing prices, data-driven algorithms can now quickly monitor competitors’ prices and adjust their own prices accordingly. So what is seemingly beneficial—increased price transparency—ironically can end up harming consumers....

Airbnb Supply and Demand - Dynamic Pricing [Live Q&A]

This is a MUST WATCH webinar!
In the wrap up of our pricing series, you will learn how to:
- Understand the basics of Airbnb supply and demand
- Identify the...

This is a MUST WATCH webinar!
In the wrap up of our pricing series, you will learn how to:
- Understand the basics of Airbnb supply and demand
- Identify the 3 BIGGEST factors that drive demand in YOUR area
Here is the link to the special offer. www.beyondpricing.com/#r=LEARNAIRBNB
This week's webinar was presented by Ian McHenry from Beyond Pricing.
Also, check out this blog post on How much your Airbnb unit is worth: http://learnairbnb.com/my-airbnb-unit-worth/

This is a MUST WATCH webinar!
In the wrap up of our pricing series, you will learn how to:
- Understand the basics of Airbnb supply and demand
- Identify the 3 BIGGEST factors that drive demand in YOUR area
Here is the link to the special offer. www.beyondpricing.com/#r=LEARNAIRBNB
This week's webinar was presented by Ian McHenry from Beyond Pricing.
Also, check out this blog post on How much your Airbnb unit is worth: http://learnairbnb.com/my-airbnb-unit-worth/

Analytics for an Online Retailer: Demand forecasting and Price Optimization at Rue La La
We partnered with the online flash sales retailer Rue La La to develop and implement a pricing decision support tool that sets initial prices for new products. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts (typically 2-3 days) on designer apparel and accessories. Our approach is two-fold and begins with developing a demand prediction model for new products. The two biggest challenges faced when building our demand prediction model include estimating lost sales due to stockouts, and predicting demand for styles that have no historical sales data. We use descriptive analytics (clustering) and predictive analytics (regression) to address these challenges and predict future demand. Regression trees - an intuitive, yet non-parametric regression model - prove to be effective predictors of demand.
We then formulate a price optimization model to maximize revenue of new products using demand predictions from the regression trees as inputs. In this case, the biggest challenge we face is that each style’s demand depends on the price of competing styles, which restricts us from solving a price optimization problem individually for each style and leads to an exponential number of variables in the price optimization problem. Furthermore, the non-parametric structure of regression trees makes this problem particularly difficult to solve. We employ prescriptive analytics by developing a novel reformulation of the price optimization problem and creating an efficient algorithm that allows Rue La La to optimize prices on a daily basis for the next day’s sales.
To implement our price optimization algorithm, we developed a fully-automated pricing decision support tool that runs automatically every day, providing price increase recommendations to merchants for events starting the next day. To estimate the tool’s impact, we conducted a field experiment on approximately 6,000 styles from mid-January through May 2014 to address the following two questions: (i) would implementing the tool’s recommended price increases cause a decrease in sales, and (ii) what impact would the price increases have on revenue?
For our field experiment, we used the Wilcoxon rank sum test to test the null hypothesis that raising prices according to the pricing decision support tool's recommendations has no negative impact on sales. We performed this test on styles in different price ranges, and the results suggest that raising prices only negatively impacts sales for very low-priced styles; for other price points, our results suggest that raising prices according to the tool’s recommendations does not decrease sales. Furthermore, we estimate an increase in revenue due to accepting our tool’s recommended price increases to be approximately 10% with a 90% confidence interval of [3%, 18%].
Since the conclusion of our field experiment, we have been extending our research to a dynamic pricing setting where Rue La La changes the price of a style over the course of their short selling season. As common in retailing, Rue La La has limited inventory and does not know the consumer's purchase probability at a given price and thus must learn this probability from sales data. We propose an efficient and effective dynamic pricing algorithm, which builds upon the well-known Thompson sampling algorithm used for multi-armed bandit problems by creatively incorporating inventory constraints into the model and algorithm. Our algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for the same setting.
Our collaboration with Rue La La shows that combining machine learning (descriptive and predictive analytics) and optimization (prescriptive analytics) into a pricing decision support tool has made a positive financial impact on Rue La La's business. We hope that the success of this work motivates retailers to investigate similar techniques to help set initial prices of new products, and, more broadly, that researchers and practitioners will use a combination of analytics techniques to harness their data and use it to improve business processes.

Analytics for an Online Retailer: Demand forecasting and Price Optimization at Rue La La
We partnered with the online flash sales retailer Rue La La to develop and implement a pricing decision support tool that sets initial prices for new products. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts (typically 2-3 days) on designer apparel and accessories. Our approach is two-fold and begins with developing a demand prediction model for new products. The two biggest challenges faced when building our demand prediction model include estimating lost sales due to stockouts, and predicting demand for styles that have no historical sales data. We use descriptive analytics (clustering) and predictive analytics (regression) to address these challenges and predict future demand. Regression trees - an intuitive, yet non-parametric regression model - prove to be effective predictors of demand.
We then formulate a price optimization model to maximize revenue of new products using demand predictions from the regression trees as inputs. In this case, the biggest challenge we face is that each style’s demand depends on the price of competing styles, which restricts us from solving a price optimization problem individually for each style and leads to an exponential number of variables in the price optimization problem. Furthermore, the non-parametric structure of regression trees makes this problem particularly difficult to solve. We employ prescriptive analytics by developing a novel reformulation of the price optimization problem and creating an efficient algorithm that allows Rue La La to optimize prices on a daily basis for the next day’s sales.
To implement our price optimization algorithm, we developed a fully-automated pricing decision support tool that runs automatically every day, providing price increase recommendations to merchants for events starting the next day. To estimate the tool’s impact, we conducted a field experiment on approximately 6,000 styles from mid-January through May 2014 to address the following two questions: (i) would implementing the tool’s recommended price increases cause a decrease in sales, and (ii) what impact would the price increases have on revenue?
For our field experiment, we used the Wilcoxon rank sum test to test the null hypothesis that raising prices according to the pricing decision support tool's recommendations has no negative impact on sales. We performed this test on styles in different price ranges, and the results suggest that raising prices only negatively impacts sales for very low-priced styles; for other price points, our results suggest that raising prices according to the tool’s recommendations does not decrease sales. Furthermore, we estimate an increase in revenue due to accepting our tool’s recommended price increases to be approximately 10% with a 90% confidence interval of [3%, 18%].
Since the conclusion of our field experiment, we have been extending our research to a dynamic pricing setting where Rue La La changes the price of a style over the course of their short selling season. As common in retailing, Rue La La has limited inventory and does not know the consumer's purchase probability at a given price and thus must learn this probability from sales data. We propose an efficient and effective dynamic pricing algorithm, which builds upon the well-known Thompson sampling algorithm used for multi-armed bandit problems by creatively incorporating inventory constraints into the model and algorithm. Our algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for the same setting.
Our collaboration with Rue La La shows that combining machine learning (descriptive and predictive analytics) and optimization (prescriptive analytics) into a pricing decision support tool has made a positive financial impact on Rue La La's business. We hope that the success of this work motivates retailers to investigate similar techniques to help set initial prices of new products, and, more broadly, that researchers and practitioners will use a combination of analytics techniques to harness their data and use it to improve business processes.

https://www.facebook.com/tusharroy25
https://github.com/mission-peace/interview/blob/master/src/com/interview/dynamic/StockBuySellKTransactions.java
https://github.com/mission-peace/interview/blob/master/python/dynamic/stockbuysellktransactions.py
https://github.com/mission-peace/interview/wiki
Given an array for which the ith element is the price of a given stock on day i.
Design an algorithm to find the maximum profit. You may complete at most k transactions. Transactions cannot occur in parallel. One transaction should complete before starting another transaction.

https://www.facebook.com/tusharroy25
https://github.com/mission-peace/interview/blob/master/src/com/interview/dynamic/StockBuySellKTransactions.java
https://github.com/mission-peace/interview/blob/master/python/dynamic/stockbuysellktransactions.py
https://github.com/mission-peace/interview/wiki
Given an array for which the ith element is the price of a given stock on day i.
Design an algorithm to find the maximum profit. You may complete at most k transactions. Transactions cannot occur in parallel. One transaction should complete before starting another transaction.

Lecture 6: Pricing Options with Monte Carlo

Lecturer: Prof. Shimon Benninga
We show how to price Asian and barrier options using MC. A starting point is an extended example of how to use MC to price pla...

Lecturer: Prof. Shimon Benninga
We show how to price Asian and barrier options using MC. A starting point is an extended example of how to use MC to price plain vanilla calls. This example illustrates the basic principles of MC pricing for options.

Lecturer: Prof. Shimon Benninga
We show how to price Asian and barrier options using MC. A starting point is an extended example of how to use MC to price plain vanilla calls. This example illustrates the basic principles of MC pricing for options.

Originally aired June 24, 2014. In this webcast you will learn how Python can be used for Derivatives Analytics and Financial Engineering. Dr. Yves J. Hilpisch will begin by covering the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. The approach is a practical one in that all-important aspects are illustrated by a set of self-contained Python scripts.
This webcast talk will cover:
Financial Algorithm Implementation
Monte CarloValuation
Binomial Option Pricing
PerformanceLibrariesDynamic Compiling
ParallelCodeExecution
DX Analytics
Overview and Philosophy
Multi-Risk Derivatives Pricing
Global Valuation
Web Technologies for Derivative Analytics
About Yves Hilpisch
Yves Hilpisch has 10 years of experience with Python, particularly in the finance space. He founded The Python Quants GmbH - an independent, privately-owned analytics software provider and financial engineering boutique. He lectures on Mathematical Finance at Saarland University in Germany and is a regular speaker at Python and Finance conferences.

Originally aired June 24, 2014. In this webcast you will learn how Python can be used for Derivatives Analytics and Financial Engineering. Dr. Yves J. Hilpisch will begin by covering the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. The approach is a practical one in that all-important aspects are illustrated by a set of self-contained Python scripts.
This webcast talk will cover:
Financial Algorithm Implementation
Monte CarloValuation
Binomial Option Pricing
PerformanceLibrariesDynamic Compiling
ParallelCodeExecution
DX Analytics
Overview and Philosophy
Multi-Risk Derivatives Pricing
Global Valuation
Web Technologies for Derivative Analytics
About Yves Hilpisch
Yves Hilpisch has 10 years of experience with Python, particularly in the finance space. He founded The Python Quants GmbH - an independent, privately-owned analytics software provider and financial engineering boutique. He lectures on Mathematical Finance at Saarland University in Germany and is a regular speaker at Python and Finance conferences.

Shoppers with Internet access and a bargain-hunting impulse can find a universe of products at their fingertips. In this thought-provoking exposé, Maurice Stucke and Ariel Ezrachi invite us to take a harder look at today’s app-assisted paradise of digital shopping. While consumers reap many benefits from online purchasing, the sophisticated algorithms and data-crunching that make browsing so convenient are also changing the nature of market competition, and not always for the better.
Computers colluding is one danger. Although long-standing laws prevent companies from fixing prices, data-driven algorithms can now quickly monitor competitors’ prices and adjust their own prices accordingly. So what is seemingly beneficial—increased price transparency—ironically can end up harming consumers. A second danger is behavioral discrimination. Here, companies track and profile consumers to get them to buy goods at the highest price they are willing to pay. The rise of super-platforms and their “frenemy” relationship with independent app developers raises a third danger. By controlling key platforms (such as the operating system of smartphones), data-driven monopolies dictate the flow of personal data and determine who gets to exploit potential buyers.
VirtualCompetition raises timely questions. To what extent does the “invisible hand” still hold sway? In markets continually manipulated by bots and algorithms, is competitive pricing an illusion? Can our current laws protect consumers? The changing market reality is already shifting power into the hands of the few. Ezrachi and Stucke explore the resulting risks to competition, our democratic ideals, and our economic and overall well-being.
About Maurice
Professor Stucke brought 13 years of litigation experience when he joined the UT College of Law faculty in 2007. As a trial attorney at the U.S.Department of Justice, AntitrustDivision, he successfully challenged anticompetitive mergers and restraints in numerous industries, and focused on policy issues involving antitrust and the media. As a SpecialAssistantU.S. Attorney, he prosecuted a variety of felony and misdemeanor offenses, including running a weekly docket before the HonorableThomas Rawles Jones, Jr. As an associate at Sullivan & Cromwell, Professor Stucke assisted in defending Goldman Sachs, CS First Boston, and Microsoft in civil antitrust litigation. The Legal Aid Society presented him two awards for his criminal appellate and defense work.
Since coming to UT, Professor Stucke has been a prolific legal scholar. His scholarship re-examines much of the conventional wisdom in competition policy in light of the empirical findings from behavioral economics and psychology. In re-evaluating the goals and assumptions of competition law, he seeks to provide policymakers with a more empirical approach to competition policy. Professor Stucke’s scholarship, which has been cited by the U.S. federal courts, the OECD, the United Nations, competition agencies and policymakers, is already impacting competition policy. He was invited by the OECD and competition authorities from the European Union, Ireland, Italy, the Netherlands, Norway, South Korea, United States, and United Kingdom to discuss his research, and has been invited to present his research at over 60 conferences in Australia, Belgium, China, England, France, Germany, Greece, Ireland, Israel, Italy, Netherlands, Norway, Sweden, Turkey, United Kingdom, and United States.
Professor Stucke serves as a Senior Fellow at the American Antitrust Institute, an independent Washington, D.C.-based non-profit education, research, and advocacy organization devoted to competition policy. Professor Stucke chaired a committee on the media industry that drafted a transition report for the incoming Obama administration. In 2009, Professor Stucke was elected as a member to the AcademicSociety for Competition Law, appointed to the advisory board of the Institute for Consumer Antitrust Studies, and was asked to serve as one of the United States’ non-governmental advisors to the International Competition Network, the only international body devoted exclusively to competition law enforcement and whose members represent national and multinational governmental competition authorities in over 100 jurisdictions.
He has co-authored two books, Big Data and Competition Policy (Oxford University Press 2016) and Virtual Competition (Harvard University Press 2016), which has been featured in The New Yorker, Wall Street Journal, Guardian, New York Review of Books, Harvard Business Review, and Wired.
More info on this event here:
https://cyber.harvard.edu/events/luncheons/2017/03/Stucke

Shoppers with Internet access and a bargain-hunting impulse can find a universe of products at their fingertips. In this thought-provoking exposé, Maurice Stucke and Ariel Ezrachi invite us to take a harder look at today’s app-assisted paradise of digital shopping. While consumers reap many benefits from online purchasing, the sophisticated algorithms and data-crunching that make browsing so convenient are also changing the nature of market competition, and not always for the better.
Computers colluding is one danger. Although long-standing laws prevent companies from fixing prices, data-driven algorithms can now quickly monitor competitors’ prices and adjust their own prices accordingly. So what is seemingly beneficial—increased price transparency—ironically can end up harming consumers. A second danger is behavioral discrimination. Here, companies track and profile consumers to get them to buy goods at the highest price they are willing to pay. The rise of super-platforms and their “frenemy” relationship with independent app developers raises a third danger. By controlling key platforms (such as the operating system of smartphones), data-driven monopolies dictate the flow of personal data and determine who gets to exploit potential buyers.
VirtualCompetition raises timely questions. To what extent does the “invisible hand” still hold sway? In markets continually manipulated by bots and algorithms, is competitive pricing an illusion? Can our current laws protect consumers? The changing market reality is already shifting power into the hands of the few. Ezrachi and Stucke explore the resulting risks to competition, our democratic ideals, and our economic and overall well-being.
About Maurice
Professor Stucke brought 13 years of litigation experience when he joined the UT College of Law faculty in 2007. As a trial attorney at the U.S.Department of Justice, AntitrustDivision, he successfully challenged anticompetitive mergers and restraints in numerous industries, and focused on policy issues involving antitrust and the media. As a SpecialAssistantU.S. Attorney, he prosecuted a variety of felony and misdemeanor offenses, including running a weekly docket before the HonorableThomas Rawles Jones, Jr. As an associate at Sullivan & Cromwell, Professor Stucke assisted in defending Goldman Sachs, CS First Boston, and Microsoft in civil antitrust litigation. The Legal Aid Society presented him two awards for his criminal appellate and defense work.
Since coming to UT, Professor Stucke has been a prolific legal scholar. His scholarship re-examines much of the conventional wisdom in competition policy in light of the empirical findings from behavioral economics and psychology. In re-evaluating the goals and assumptions of competition law, he seeks to provide policymakers with a more empirical approach to competition policy. Professor Stucke’s scholarship, which has been cited by the U.S. federal courts, the OECD, the United Nations, competition agencies and policymakers, is already impacting competition policy. He was invited by the OECD and competition authorities from the European Union, Ireland, Italy, the Netherlands, Norway, South Korea, United States, and United Kingdom to discuss his research, and has been invited to present his research at over 60 conferences in Australia, Belgium, China, England, France, Germany, Greece, Ireland, Israel, Italy, Netherlands, Norway, Sweden, Turkey, United Kingdom, and United States.
Professor Stucke serves as a Senior Fellow at the American Antitrust Institute, an independent Washington, D.C.-based non-profit education, research, and advocacy organization devoted to competition policy. Professor Stucke chaired a committee on the media industry that drafted a transition report for the incoming Obama administration. In 2009, Professor Stucke was elected as a member to the AcademicSociety for Competition Law, appointed to the advisory board of the Institute for Consumer Antitrust Studies, and was asked to serve as one of the United States’ non-governmental advisors to the International Competition Network, the only international body devoted exclusively to competition law enforcement and whose members represent national and multinational governmental competition authorities in over 100 jurisdictions.
He has co-authored two books, Big Data and Competition Policy (Oxford University Press 2016) and Virtual Competition (Harvard University Press 2016), which has been featured in The New Yorker, Wall Street Journal, Guardian, New York Review of Books, Harvard Business Review, and Wired.
More info on this event here:
https://cyber.harvard.edu/events/luncheons/2017/03/Stucke

How Amazon’s Algorithm Gets You to Spend Money

Companies like Amazon take advantage of the fact that they know a whole lot more about buying patterns than you do. As author and entrepreneur Jerry Kaplan explains, this sort of information asymmetry is the real crux of their business plan. Jerry Kaplan's latest book is "Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence" (http://goo.gl/bSVV8K).
Read more at BigThink.com: http://bigthink.com/videos/jerry-kaplan-on-how-amazon-gets-your-money
FollowBigThink here:
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Transcript - If you’ve ever been online and if you haven’t I don’t know what you’re doing watching this video. You know that many websites are tracking and studying your behavior and in a way they help you by presenting products and information that they think that they believe based upon your browsing history and other characteristics are going to be of great interest to you. But there’s also a darker side to that activity. While that may add great convenience to you the truth is that that also permits them to look at questions like what do they estimate you’re willing to pay for that product? Now a lot of people think mistakenly that you’re supposed to charge the same price for a product to everybody. That’s not the case. You can’t discriminate based on certain criteria – race, religion, sexual preference. But it’s perfectly fine for me to charge this guy more than that guy because I think he’ll pay more and just look at airplane tickets as a perfect example of that sort of thing. Now here’s the problem. We’re taking those kinds of decisions in these websites. Amazon itself is a fantastic example of this and we’re incorporating very sophisticated machine run algorithms that are designed to manage the overall behavior of the group of people who are visiting that website.
In order to optimize profitability for the companies that are running those websites. And they will cut you the least slice of pie, small slice of pie that they can to get you to send you to do what they want you to do in order to maximize the profits of the corporation. Now you may have been on Amazon and you may put things in – I use what’s called a save for later or something in your cart. And you come back the next day and good news, you know, this book is three cents less or that’s two cents more or this is a dollar more. But there aren’t people doing that. This is a machine learning algorithm. And what it’s doing is analyzing time of day and the characteristics of what you bought in the past and how you’ve respond to different kinds of incentives. And where you came from and what kind of browser you’re using as a major factor. Anything it can in order to adjust the price to just the point where you’re going to buy at the highest possible price. You as an individual have freedom of choice. It’s a free country. Buy it. You cannot buy it. That’s great. But we as a group as a set of customers purchasing from Amazon or some other site we adhere to certain statistical properties. So as a group we don’t have that freedom because it can be managed by the entity on the other side. Whenever there’s an information asymmetry like that they know what you’re likely to buy by what your characteristics are and they can optimize the yield on site based upon that. They’re at an advantage over you. Amazon is a wonderful company but it is basically one giant machine learning algorithm. It is designed to do what’s called arbitrage. It knows what it can buy things for. It knows what it can sell things for. And it can adjust the profitability in that zone in order to maximize sales, in order to maximize profits.
And it can do so in a way that is far more efficient than has ever been possible in retailing before. So when I think of Amazon the fact that they’re selling goods is incidental. I think of it like a stock trading programs. Buy low, sell high. Buy here, sell there. There’s a spread. These really are arbitrage systems and you are the mechanism by which these companies maximize their profits.

Pricing Analytics: Optimizing Price

The “best” price for a product or service is one that maximizes profits, not necessarily the price that sells the most units. This presentation uses real-world examples to explore how Excel’s Solver functionality can be used to calculate the optimal price for any product or service.
Downloadable slides are available from SlideShare at http://goo.gl/yGmGfq

2:08

Insights on Amazon's Dynamic Pricing

In this new video, Mabel McLean speaks about how two of retail’s largest players are compe...

Insights on Amazon's Dynamic Pricing

In this new video, Mabel McLean speaks about how two of retail’s largest players are competing against each other. Though both have reputations as low-cost players, Amazon is making unprecedented investments in fulfillment. In the past year, it rolled out free two-day shipping for Prime members and same-day shipping in six US cities. In the summer of 2014, the rollout of Prime Pantry enabled Amazon to ship CPG products profitably in bulk.
Meanwhile, Walmart has adopted an omnichannel strategy. Its ship-to-store program allows consumers to get free shipping in exchange for picking up their items at a store. The brand is also piloting Walmart To-Go, a program that allows users to order their groceries online and pick up at the store in a two to three-hour window.
McLean believes an omnichannel strategy is more effective than one focused on online fulfillment.
View More: http://goo.gl/IIk6LV
Signup for updates: http://goo.gl/kVJTQ3
Follow us on LinkedIn: https://www.linkedin.com/company/l2-inc-
Follow us on Twitter: https://twitter.com/L2_Digital
Follow us on Facebook: https://www.facebook.com/l2inc

11:33

Predicting Stock Price Mathematically

There are two prices that are critical for any investor to know: the current price of the ...

Predicting Stock Price Mathematically

There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "MindMath" from Dr. Garg
https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

10:03

The science behind airfare pricing

Airline fares often seem random, but Richard Quest is on a mission to discover the truth b...

Michael Rechenthin, Ph.D. (Dr. Data) explained how all models rely on previously known data such as market prices. Last week in the segment on July 7, 2016, "Predicting (Extrapolating) the Future" Dr. Data explained the science based method of attempting to predict the future which is known as predictive modeling. Today’s segment is part 1 of a planned two part series. Mike uses his visual tools of Data Science to make the intimidating subject of regression algorithms and what information we can glean from them that is most important, easily understood.
A list of information used in models to help them predict future price was displayed in a table. The types of information listed were, historical price data, sector the underlying is in, near a yearly high/low, historical historical Implied Volatility (IV), past month’s price range, news out, near earnings, and outside of the 1 Standard Deviation price range. Dr. Data then noted that most researchers start with regression algorithms. They try to predict prices in the future, while minimizing any errors.
Using a series of graphs Mike contrasted two different models. One was much better at coming closer to the actual price. It’serror, the eventual actual price minus the predicted price was small. Dr. Data explained that the second model, in which the error level was much higher, was actually much better because it more accurately predicted direction. Mike demonstrated why direction is much more important. Predicting the magnitude of the move correctly is great but we can be way off on the magnitude and still make money as long as we get direction right. That’s the big problem with regression algorithms.
Watch this segment of The Skinny On Options Data Science with Tom Sosnoff, Tony Battista and Dr. Data (Michael Rechenthin, Ph.D) as Mike explains the weakness of regression algorithms and the importance of direction over a lower magnitude of error and he does it in a way that is understandable to all tastytraders.
======== tastytrade.com ========
Hosted by Tom Sosnoff and Tony Battista, tastytrade is a real financial network with 8 hours of live programming five days a week during market hours. From pop culture to advanced investment strategies, tastytrade has a broad spectrum of content for viewers of all kinds! Tune in and learn how to trade options successfully and make the most of your investments! Watch tastytrade LIVE daily Monday-Friday 7am-3:30pmCT: http://ow.ly/EbzUU
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Linear Programming - Shadow Price, Slack/Surplus calculations

This video shows how to solve the following problem.
Min Z = 5x1 + x2
s.t.
2x1 + x2 ≥ 6
X1 + x2 ≥ 4
2x1 + 10x2 ≥ 20
X1, x2 ≥ 0
a) Graphically solve the linear programming problem and determine the optimal solution.
b) What is the objective function value?
c) Calculate slack/surplus for each constraint.
d) If the RHS of Constraint #1 increases by 1, by how much would the OFV change?
e) If the RHS of Constraint #2 increases by 2, by how much would the OFV change?
f) Suppose x1 and x2 are required to be integers, what will the optimal solution be?

Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb

VP of Product, Joe Zadeh, talks to updates to the calendar function and reveals Airbnb's new Smart Pricing function and explains how it can help hosts set more informed prices throughout the year to help them maximize their income potential.
SUBSCRIBE: https://www.youtube.com/channel/UCCww-R0oM_CQWXerBcNyKKw?sub_confirmation=1
About Airbnb:
Airbnb is a platform that connects people from around the world to incredible places to stay and interesting things to do. Whether an apartment for a night, a castle for a week, or salsa lessons in Havana to help you truly live there. The Airbnb community aims to create a world where all 7.5 billion people can belong anywhere.
Connect with Airbnb:
Visit Airbnb Website: https://www.airbnb.com
Like Airbnb on FACEBOOK: https://www.facebook.com/airbnb/
Follow Airbnb on TWITTER: https://twitter.com/airbnb
Follow Airbnb on INSTAGRAM: https://www.instagram.com/airbnb/
Follow Airbnb on LINKEDIN: https://www.linkedin.com/company-beta/309694/
Follow Airbnb on PINTEREST: https://www.pinterest.com/airbnb/
Joe Zadeh on Smart Pricing | Airbnb Open | Airbnb
https://www.youtube.com/user/Airbnb

12:37

Airline Pricing - Part 1 - Fare Structures

We discuss how airlines maximize revenue by creating several fares in the same market

FROM BIG DATA TO THE ALGORITHMIC ECONOMY, VICTOR ROSENMAN

Victor Rosenman is CEO & Founder of Feedvisor. Before founding Feedvisor, Victor worked as a consultant to high-volume Amazon sellers, helping optimize and automate many aspects of their business. This deep technical understanding of the Amazon Marketplace, coupled with a strong knowledge of computer algorithms, led Victor to establish Feedvisor in 2011.
Feedvisor is the pioneer of Algo-Commerce – the discipline of using Big Data and Machine Learning Algorithms to make business-critical decisions for online retailers.
Feedvisor’s cloud-based Algorithmic Repricing and RevenueIntelligence solutions power millions of pricing decisions daily; providing retailers with actionable insights to maximize profitability and drive their business growth.
www.feedvisor.com

3:58

Ramsey Theory: An Introduction

This video is created as a study project by Class Math 303 Group 1B from Simon Fraser Univ...

Airbnb Supply and Demand - Dynamic Pricing [Live Q&A]

This is a MUST WATCH webinar!
In the wrap up of our pricing series, you will learn how to:
- Understand the basics of Airbnb supply and demand
- Identify the 3 BIGGEST factors that drive demand in YOUR area
Here is the link to the special offer. www.beyondpricing.com/#r=LEARNAIRBNB
This week's webinar was presented by Ian McHenry from Beyond Pricing.
Also, check out this blog post on How much your Airbnb unit is worth: http://learnairbnb.com/my-airbnb-unit-worth/

36:20

Dynamic Pricing - Airplane (Matlab Simulation)

Dynamic Pricing - Example of pricing for an airplane using Matlab for simulation

2015 IAAA Finalist Analytics for an Online Retailer

Analytics for an Online Retailer: Demand forecasting and Price Optimization at Rue La La
We partnered with the online flash sales retailer Rue La La to develop and implement a pricing decision support tool that sets initial prices for new products. Rue La La is in the online fashion sample sales industry, where they offer extremely limited-time discounts (typically 2-3 days) on designer apparel and accessories. Our approach is two-fold and begins with developing a demand prediction model for new products. The two biggest challenges faced when building our demand prediction model include estimating lost sales due to stockouts, and predicting demand for styles that have no historical sales data. We use descriptive analytics (clustering) and predictive analytics (regression) to address these challenges and predict future demand. Regression trees - an intuitive, yet non-parametric regression model - prove to be effective predictors of demand.
We then formulate a price optimization model to maximize revenue of new products using demand predictions from the regression trees as inputs. In this case, the biggest challenge we face is that each style’s demand depends on the price of competing styles, which restricts us from solving a price optimization problem individually for each style and leads to an exponential number of variables in the price optimization problem. Furthermore, the non-parametric structure of regression trees makes this problem particularly difficult to solve. We employ prescriptive analytics by developing a novel reformulation of the price optimization problem and creating an efficient algorithm that allows Rue La La to optimize prices on a daily basis for the next day’s sales.
To implement our price optimization algorithm, we developed a fully-automated pricing decision support tool that runs automatically every day, providing price increase recommendations to merchants for events starting the next day. To estimate the tool’s impact, we conducted a field experiment on approximately 6,000 styles from mid-January through May 2014 to address the following two questions: (i) would implementing the tool’s recommended price increases cause a decrease in sales, and (ii) what impact would the price increases have on revenue?
For our field experiment, we used the Wilcoxon rank sum test to test the null hypothesis that raising prices according to the pricing decision support tool's recommendations has no negative impact on sales. We performed this test on styles in different price ranges, and the results suggest that raising prices only negatively impacts sales for very low-priced styles; for other price points, our results suggest that raising prices according to the tool’s recommendations does not decrease sales. Furthermore, we estimate an increase in revenue due to accepting our tool’s recommended price increases to be approximately 10% with a 90% confidence interval of [3%, 18%].
Since the conclusion of our field experiment, we have been extending our research to a dynamic pricing setting where Rue La La changes the price of a style over the course of their short selling season. As common in retailing, Rue La La has limited inventory and does not know the consumer's purchase probability at a given price and thus must learn this probability from sales data. We propose an efficient and effective dynamic pricing algorithm, which builds upon the well-known Thompson sampling algorithm used for multi-armed bandit problems by creatively incorporating inventory constraints into the model and algorithm. Our algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for the same setting.
Our collaboration with Rue La La shows that combining machine learning (descriptive and predictive analytics) and optimization (prescriptive analytics) into a pricing decision support tool has made a positive financial impact on Rue La La's business. We hope that the success of this work motivates retailers to investigate similar techniques to help set initial prices of new products, and, more broadly, that researchers and practitioners will use a combination of analytics techniques to harness their data and use it to improve business processes.

https://www.facebook.com/tusharroy25
https://github.com/mission-peace/interview/blob/master/src/com/interview/dynamic/StockBuySellKTransactions.java
https://github.com/mission-peace/interview/blob/master/python/dynamic/stockbuysellktransactions.py
https://github.com/mission-peace/interview/wiki
Given an array for which the ith element is the price of a given stock on day i.
Design an algorithm to find the maximum profit. You may complete at most k transactions. Transactions cannot occur in parallel. One transaction should complete before starting another transaction.

2:06:50

Lecture 6: Pricing Options with Monte Carlo

Lecturer: Prof. Shimon Benninga
We show how to price Asian and barrier options using MC. ...

Lecture 6: Pricing Options with Monte Carlo

Lecturer: Prof. Shimon Benninga
We show how to price Asian and barrier options using MC. A starting point is an extended example of how to use MC to price plain vanilla calls. This example illustrates the basic principles of MC pricing for options.

Derivatives Analytics with Python - O'Reilly Webcast

Originally aired June 24, 2014. In this webcast you will learn how Python can be used for Derivatives Analytics and Financial Engineering. Dr. Yves J. Hilpisch will begin by covering the necessary background information, theoretical foundations and numerical tools to implement a market-based valuation of stock index options. The approach is a practical one in that all-important aspects are illustrated by a set of self-contained Python scripts.
This webcast talk will cover:
Financial Algorithm Implementation
Monte CarloValuation
Binomial Option Pricing
PerformanceLibrariesDynamic Compiling
ParallelCodeExecution
DX Analytics
Overview and Philosophy
Multi-Risk Derivatives Pricing
Global Valuation
Web Technologies for Derivative Analytics
About Yves Hilpisch
Yves Hilpisch has 10 years of experience with Python, particularly in the finance space. He founded The Python Quants GmbH - an independent, privately-owned analytics software provider and financial engineering boutique. He lectures on Mathematical Finance at Saarland University in Germany and is a regular speaker at Python and Finance conferences.

Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy

Shoppers with Internet access and a bargain-hunting impulse can find a universe of products at their fingertips. In this thought-provoking exposé, Maurice Stucke and Ariel Ezrachi invite us to take a harder look at today’s app-assisted paradise of digital shopping. While consumers reap many benefits from online purchasing, the sophisticated algorithms and data-crunching that make browsing so convenient are also changing the nature of market competition, and not always for the better.
Computers colluding is one danger. Although long-standing laws prevent companies from fixing prices, data-driven algorithms can now quickly monitor competitors’ prices and adjust their own prices accordingly. So what is seemingly beneficial—increased price transparency—ironically can end up harming consumers. A second danger is behavioral discrimination. Here, companies track and profile consumers to get them to buy goods at the highest price they are willing to pay. The rise of super-platforms and their “frenemy” relationship with independent app developers raises a third danger. By controlling key platforms (such as the operating system of smartphones), data-driven monopolies dictate the flow of personal data and determine who gets to exploit potential buyers.
VirtualCompetition raises timely questions. To what extent does the “invisible hand” still hold sway? In markets continually manipulated by bots and algorithms, is competitive pricing an illusion? Can our current laws protect consumers? The changing market reality is already shifting power into the hands of the few. Ezrachi and Stucke explore the resulting risks to competition, our democratic ideals, and our economic and overall well-being.
About Maurice
Professor Stucke brought 13 years of litigation experience when he joined the UT College of Law faculty in 2007. As a trial attorney at the U.S.Department of Justice, AntitrustDivision, he successfully challenged anticompetitive mergers and restraints in numerous industries, and focused on policy issues involving antitrust and the media. As a SpecialAssistantU.S. Attorney, he prosecuted a variety of felony and misdemeanor offenses, including running a weekly docket before the HonorableThomas Rawles Jones, Jr. As an associate at Sullivan & Cromwell, Professor Stucke assisted in defending Goldman Sachs, CS First Boston, and Microsoft in civil antitrust litigation. The Legal Aid Society presented him two awards for his criminal appellate and defense work.
Since coming to UT, Professor Stucke has been a prolific legal scholar. His scholarship re-examines much of the conventional wisdom in competition policy in light of the empirical findings from behavioral economics and psychology. In re-evaluating the goals and assumptions of competition law, he seeks to provide policymakers with a more empirical approach to competition policy. Professor Stucke’s scholarship, which has been cited by the U.S. federal courts, the OECD, the United Nations, competition agencies and policymakers, is already impacting competition policy. He was invited by the OECD and competition authorities from the European Union, Ireland, Italy, the Netherlands, Norway, South Korea, United States, and United Kingdom to discuss his research, and has been invited to present his research at over 60 conferences in Australia, Belgium, China, England, France, Germany, Greece, Ireland, Israel, Italy, Netherlands, Norway, Sweden, Turkey, United Kingdom, and United States.
Professor Stucke serves as a Senior Fellow at the American Antitrust Institute, an independent Washington, D.C.-based non-profit education, research, and advocacy organization devoted to competition policy. Professor Stucke chaired a committee on the media industry that drafted a transition report for the incoming Obama administration. In 2009, Professor Stucke was elected as a member to the AcademicSociety for Competition Law, appointed to the advisory board of the Institute for Consumer Antitrust Studies, and was asked to serve as one of the United States’ non-governmental advisors to the International Competition Network, the only international body devoted exclusively to competition law enforcement and whose members represent national and multinational governmental competition authorities in over 100 jurisdictions.
He has co-authored two books, Big Data and Competition Policy (Oxford University Press 2016) and Virtual Competition (Harvard University Press 2016), which has been featured in The New Yorker, Wall Street Journal, Guardian, New York Review of Books, Harvard Business Review, and Wired.
More info on this event here:
https://cyber.harvard.edu/events/luncheons/2017/03/Stucke

Asset Pricing (2017) Week 1 class (Mean-variance a...

Undergrad Research: Option Pricing via Machine Lea...

It turns out that a theory explaining how we might detect parallel universes and prediction for the end of the world was proposed and completed by physicist Stephen Hawking shortly before he died ... &nbsp;. According to reports, the work predicts that the universe would eventually end when stars run out of energy ... ....

In another blow to the Trump administration Monday, the US Supreme Court decided Arizona must continue to issue state driver’s licenses to so-called Dreamer immigrants and refused to hear an effort by the state to challenge the Obama-era program that protects hundreds of thousands of young adults brought into the country illegally as children, Reuters reported ... – WN.com. Jack Durschlag....

Uber announced on Monday that it was pulling all of its self-driving cars from public roads in Arizona and San Francisco, Toronto, and Pittsburgh after a female pedestrian was reportedly killed after being struck by an autonomous Uber vehicle in Tempe, according to The Verge.&nbsp; ... “We are fully cooperating with local authorities in their investigation of this incident.” ... "Some incredibly sad news out of Arizona....

An explosion on Sunday night in Austin shared "similarities" with three bombs that went off in the Texas capital earlier this month and authorities were warning on Monday that they are dealing with a serial bomber who is targeting the city, according to the Washington Post... “So we’ve definitely seen a change in the method that this suspect … is using.” ... “And we assure you that we are listening ... -WN.com, Maureen Foody....

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The action by the senator and the House members follows the decision by the Justice Department to force RT America to register as a foreign agent and the imposition of algorithms by Facebook, Google and Twitter that steer traffic away from left-wing, anti-war and progressive websites, including Truthdig ... And the situation appears to be growing worse as the algorithms are refined....

File - This Jan. 17, 2017, file photo shows a Facebook logo being displayed in a start-up companies gathering at Paris' Station F, in Paris. A former employee of a Trump-affiliated data-mining firm says it used algorithms that "took fake ... ....

They will support infrastructure managers in addressing performance and meeting consistent workload demands in data. This press release features multimedia ... Notes ... [2] IOPS ... Self Encrypting Drive (SED) supports the AES 256 bit cryptographic algorithm as one of the measurers to protect data confidentiality and safety in case of system theft or system asset disposal. FIPS-validated models support the AES 256-bit cryptographic algorithm ... Tel....

THE GOODNEWS..Overlooked stories of incredible women are being added to Wikipedia by dedicated volunteers. Movements like #MeToo are drawing increased attention to the systemic discrimination facing women in a range of professional fields, from Hollywood and journalism, to banking and government ... Wikipedia is the fifth most popular website worldwide ... Gender bias is also an ongoing issue in content development and search algorithms ... ....

TV news has gotten a bad rep, especially with younger generations. Only 8% of those between the ages of 18 and 29 are getting their news from network TV, vs. 49% of those who are ages 65 and older, according to Pew Research. But it's not just young people who may have lost interest in the 24/7 TV news coverage ... Netflix (NASDAQ ... Netflix ... Recent changes to its NewsFeedalgorithm caused the news content in users' feeds to drop to 4% from 5%....

CommissionerElizabeth Denham said in a statement Monday that she planned to seek the warrant because the British firm had been "uncooperative" in her investigation of whether Facebook data was "illegally acquired and used.". Denham said ... ———. 4.10 p.m ... ———. 3.55 p.m ... A former employee of a Trump-affiliated data-mining firm says it used algorithms that "took fake news to the next level" using data inappropriately obtained from Facebook ... ....

NEW YORK (AP) — The Latest on CambridgeAnalytica's use of Facebook data (all times local).. 8 p.m ...Channel 4 said its reporter posed as a wealthy Sri Lankan looking to hire the company ... ———. 5.50 p.m ... Denham said ... ———. 4.10 p.m ... Cambridge also earned $5.8 million from Sen ... A former employee of a Trump-affiliated data-mining firm says it used algorithms that "took fake news to the next level" using data inappropriately obtained from Facebook....

The newspaper, citing current and former employees it did not name, said AlexStamos will leave after a disagreement over how the social network should deal with its role in spreading misinformation ... ———. 8 p.m ... ———. 5.50 p.m ... Denham said ... ———. 4.10 p.m ... ———. 3.55 p.m ... ——— ... A former employee of a Trump-affiliated data-mining firm says it used algorithms that "took fake news to the next level" using data inappropriately obtained from Facebook ... ....

Biotech, robotics, and fintech startups took the spotlight today at prestigious accelerator Y Combinator’s 26th Demo Day. This batch features 141 total companies from 23 countries, with presentations spread over two days. The house was packed at Mountain View’s Computer History Museum with wealthy investors forced to stand in the back or sit on floor ...Bear Flag Robotics ... Algosurg has built algorithms to simulate surgeries ... ....

Scientists have developed a novel algorithm that may help detect and prevent cyber attacks on GPS-enabled devices in real time. The algorithm developed by researchers at University of Texas at San Antonio (UTSA) in the US mitigates the effects of spoofed GPS attacks on electrical grids and other GPS-reliant technologies ... The algorithm, which can be ......

Wanting to make their job a little easier, researchers at the University of Toronto developed a new artificial intelligence algorithm that helped them identify 6,000 previously unseen craters on Earth's moon... ....

Scientists have developed a novel algorithm that may help detect and prevent cyber attacks on GPS-enabled devices in real time. The algorithm developed by researchers at University of Texas at San Antonio (UTSA) in the US mitigates the effects of spoofed GPS attacks on electrical grids and other GPS-reliant technologies ... The algorithm, which can be ......

Monero lead developer Riccardo Spagni has emphasized that the units will not work on monero due to a scheduled hard fork designed specifically to outwit the Cryptonight algorithm... “We are pleased to announce the all-new Antminer X3, to mine cryptocurrencies based on the Cryptonight hashing algorithm,” tweeted Bitmain cheerily on March 15....